Astrina API rate limits: headers, retries, and errors

Rate-limit headers and fields to check first

Start with the response itself. If a request is rejected, the headers and metadata usually tell you more than the error text does.

Look for fields that show the reset time, the remaining allowance, and the limit category. If Astrina returns classification data, keep it in the log too, because the same endpoint can behave differently for different request types. When you are troubleshooting Astrina API rate limits, this is especially important because small differences in headers can explain why one call succeeds and the next one fails.

One small habit saves time later: record the exact timestamp of the request, not just the error. A reset value that looks “off” is often just a clock mismatch. I have seen that more than once.

If your client already stores status codes, add the relevant headers beside them. That makes it easier to spot a pattern when the ceiling is hit at 09:00 and again at 09:03. Two numbers tell the story faster than a paragraph of guesses.

On astrina, this is the kind of detail that helps an integration move from guesswork to evidence. The goal is simple: know whether you are blocked, how long the block lasts, and which request type triggered it.

Distinguishing per-user, per-key, and per-endpoint limits

Not every throttle is the same. A single user can hit a user-based limit while the same key still works for a different account, and an endpoint-specific ceiling can fail one path while the rest of the API stays open.

That distinction matters because the fix changes. If one credential is exhausted, rotating requests through another key may be appropriate. If one endpoint is capped, spreading traffic across unrelated paths will not help. If the whole account is constrained, the pattern is wider than a single token.

Check which request fails first. Then repeat the same call with a different user, a different key, or a different path, one variable at a time. Three tests, not ten, often reveal the boundary.

Think of it as a map with three locks. The lock on one door does not mean the whole building is closed. It may just be the door you chose.

A practical clue is repetition. If requests to `/search` fail while `/status` still works, that leans toward an endpoint limit. If both fail only for one credential, the limit is likely attached to that identity.

What a “rate limited” response means in practice

A “rate limited” response means the server has decided the current request is not allowed right now. It does not automatically mean the account is broken, the key is invalid, or the system is down.

Some requests may still succeed elsewhere. A read-heavy endpoint can be blocked while a lower-volume endpoint still responds normally. That is why the client should not assume the entire session is dead after one rejection.

Log the full context for each failure: endpoint, credential ID, request time, status code, and any request ID returned by the API. Without those five pieces, support has to reconstruct the scene from fragments.

One client-side mistake is to treat every rejection as the same event. It is not. A rate limit is temporary, while an authentication error usually is not. Mixing the two leads to bad retries and longer outages.

On every site you look after, the same rule applies: a blocked request should be labeled as blocked, not merely “failed.” That small label keeps dashboards honest.

Immediate client behavior after throttling

After a throttle, stop sending the same request in a tight loop. A frontend should pause that action, a backend job should move the item into a retry queue, and an integration should hold the next call until the reset time or backoff window passes.

For interactive flows, show a short message and a retry path. For jobs, prefer a queue with a clear delay field. For scripts, exit cleanly and let the scheduler try again later. Three environments, three reactions.

Do not keep hammering the same endpoint. If 20 requests are already blocked, the 21st is not more persuasive.

Preserve the original payload. If the request is safe to replay, store enough data to rebuild it exactly once the wait ends. If the request creates state, make sure the server can handle duplicates, because a delayed retry can arrive after the first attempt eventually succeeds.

This is also the place to separate user-facing latency from system behavior. A spinner can wait 5 seconds; a job runner can wait 5 minutes. The client should know which one it is.

Backoff and retry timing for short bursts

Short bursts need discipline. Start with a delay, then lengthen the wait after each failed retry using exponential backoff, and add jitter if your client supports it.

Jitter matters because synchronized retries are noisy. If 50 workers all wake up at the same second, they can create a second wave of pressure. That is how a short throttle becomes a long one.

Use a capped retry count. Five attempts may be enough for a brief burst; fifty is usually a signal that the client is ignoring the limit rather than respecting it.

A simple pattern works well: wait 1 second, then 2, then 4, then 8, while adding a small random offset. The exact numbers may differ by system, but the shape of the behavior should stay calm and predictable.

If the API publishes a reset time, prefer that over guesswork. If it does not, backoff is the safer path than aggressive polling. One extra request can be expensive when the ceiling is already in view.

Separating transient throttling from authentication or permission errors

Rate limits and access errors are cousins, not twins. A bad token usually fails all the time. A throttle fails only under pressure.

Test the same credential on a low-cost endpoint. If that request works, the token is likely valid and the issue is probably volume, not auth. If it fails with the same pattern, the problem may be permission, expiration, or a revoked key.

Watch the status code and the response body together. A limit response often has a different shape from an invalid-credentials response, even if both arrive as 4xx class errors. The body may name the limit type, while a permission issue may mention scope or access denied.

Do not rebuild the credential on the first rejection. That can waste hours. Confirm whether the failure is tied to load, to identity, or to a missing grant.

One clean diagnostic step is to compare a successful call from earlier in the day with the failing one. Same key, same endpoint, different result. That contrast usually narrows the problem quickly.

Monitoring rate-limit events in logs and alerts

Logs should answer four questions: how often, where, who, and how long. Frequency shows the scale. The affected endpoint shows the hot spot. The request ID links support to the exact call. Time-to-recovery shows whether the client waited long enough.

Alert on repeated throttles, not a single blip. One failed burst may be a user clicking twice. Ten in two minutes is a pattern worth attention.

Keep the request ID in the alert payload. Add the user, the key, or the job name if your system has them. That makes the alert useful to both engineering and support.

A clean log line might include the endpoint, status, limit category, reset time, and retry count. Five fields are enough for most investigations. More is fine, but less tends to turn into a scavenger hunt.

If you are comparing traffic across products, astrina can help keep the same activity visible in one place. That matters when one dashboard shows a gentle rise and another shows a sharp spike.

When to escalate to Astrina support

Escalate when normal traffic is still being throttled after you have verified the client behavior, the endpoint mix, and the retry timing. A limit that triggers under routine use is not something to guess about for long.

Bring evidence. Include timestamps, request IDs, the endpoint name, the credential or account reference, and the observed reset behavior. A support ticket with those five items is far easier to act on than “it keeps failing.”

Also escalate if the limit behavior looks inconsistent. If the same request is allowed at 10:01 and blocked at 10:02 with no meaningful change in volume, that deserves a closer look.

One more case stands out: if your integration is small but shared across many teams, a seemingly modest burst can look like a larger system event. In that situation, support can confirm whether the account-level ceiling is being reached or whether one job is misbehaving.

If your use case touches multiple properties, every client site in one dashboard can make the investigation cleaner, because the same limit event can be traced across accounts without bouncing between tools.

Keep the conversation specific. “We hit the ceiling three times between 14:10 and 14:18 on `/reports` with key X” is actionable. “The API is slow” is not. The first line points to a limit. The second one points to a feeling.